ColossalAI Testing: Distributed GPU Computing and Model Optimization Validation
The ColossalAI testing framework implements a comprehensive suite of unit tests using pytest, focusing on verifying critical distributed computing and model optimization functionalities. With 179 test cases, the framework thoroughly validates components like FP8 operations, bias additions, and distributed GPU communications, ensuring the reliability of ColossalAI's large-scale AI training capabilities. Qodo Tests Hub provides developers with detailed insights into ColossalAI's testing patterns, making it easier to understand how to implement robust tests for distributed AI systems. Through interactive exploration of real test implementations, developers can learn best practices for testing complex operations like model sharding, precision formats, and multi-GPU communications – essential knowledge for building reliable AI infrastructure.
| Path | Test Type | Language | Description |
|---|---|---|---|
| examples/images/diffusion/scripts/tests/test_checkpoint.py |
unit
|
python | This PyTorch unit test verifies checkpoint compatibility between custom UNet and diffusers pipeline implementations in Stable Diffusion models. |
| examples/images/diffusion/scripts/tests/test_watermark.py |
unit
|
python | This Python unit test verifies watermark detection and extraction functionality in images using DWT-DCT algorithm. |
| examples/tutorial/auto_parallel/auto_ckpt_solver_test.py |
unit
|
python | This Python unit test verifies the automatic checkpoint solver’s performance characteristics for different deep learning models in ColossalAI. |
| examples/tutorial/sequence_parallel/data/datasets/test/test_indexed_dataset.py |
unit
|
python | This Python development test verifies indexed dataset operations and document retrieval functionality in ColossalAI’s data processing pipeline. |
| tests/test_analyzer/test_fx/test_bias_addition.py |
unit
|
python | This PyTorch unit test verifies bias addition splitting behavior in neural network models using symbolic tracing. |
| tests/test_analyzer/test_fx/test_mod_dir.py |
unit
|
python | This pytest unit test verifies module directory tracking functionality in symbolic traced neural network models within ColossalAI. |
| tests/test_analyzer/test_fx/test_shape_prop.py |
unit
|
python | This PyTest unit test verifies shape propagation functionality across FX graph transformations in various deep learning models. |
| tests/test_analyzer/test_fx/test_symbolic_profile.py |
unit
|
python | This pytest unit test verifies symbolic profiling functionality for TorchVision and TIMM models in ColossalAI’s analyzer module. |
| tests/test_analyzer/test_fx/zoo.py |
unit
|
python | This Python unit test verifies the compatibility and functionality of various deep learning model architectures from torchvision and timm frameworks. |
| tests/test_analyzer/test_subclasses/test_aten.py |
unit
|
python | This PyTest unit test verifies PyTorch ATen operations compatibility with meta tensors in ColossalAI framework |